Seizure Recurrence in Children after Stopping Antiepileptic Medication: 5-Year Follow-Up

Vascular syndrome predicts the development and course of epilepsy after perinatal stroke

OBJECTIVE

Epilepsy develops in one third of the patients after perinatal stroke. It is still unclear which vascular syndrome of ischemic stroke carries higher risk of epilepsy. The aim of the current study was to evaluate the risk of epilepsy according to the vascular syndrome of perinatal stroke.

METHODS

The study included 39 children with perinatal arterial ischemic stroke (13 with anterior or posterior trunk of the distal middle cerebral artery occlusion, 23 with proximal or distal M1 middle cerebral artery occlusion and three with lenticulostriate arteria infarction), and 44 children with presumed perinatal venous infarction. Magnetic resonance imaging obtained at the chronic stage was used to evaluate the vascular syndrome of stroke.

RESULTS

The median follow-up time was 15.1 years (95% CI: 12.4-16.5 years), epilepsy developed in 19/83 (22.9%) patients. The cumulative probability to be without epilepsy at 15 years was 75.4% (95% CI: 65.8-86.4). The probability of having epilepsy was higher in the group of proximal or distal M1 artery occlusion compared to patients with periventricular venous infarction (HR 7.2, 95% CI: 2.5-26, p = .0007). Patients with periventricular venous infarction had significantly more often status epilepticus or spike-wave activation in sleep ≥85% of it compared to patients with anterior or posterior trunk of the distal middle cerebral artery occlusion (OR = 81; 95% CI: 1.3-5046, p = .029).

SIGNIFICANCE

The emphasis of this study is placed on classifying the vascular syndrome of perinatal stroke and on the targeted follow-up of patients for epilepsy until young adulthood. The risk for having epilepsy after perinatal stroke is the highest in children with proximal or distal M1 middle cerebral artery occlusion. Patients with periventricular venous infarction have a more severe course of epilepsy.

A meta-analysis of the relationship between abnormal pretreatment EEG and epilepsy recurrence

BACKGROUND

Researchers have studied the risk factors for epilepsy recurrence among patients who withdraw from antiseizure medication (ASM). These studies aimed to determine the optimal time for ASM withdrawal. EEG findings are one of the risk factors that has been studied. However, it remains unclear whether abnormal pretreatment EEG findings are a risk factor for recurrence after ASM withdrawal. We performed this meta-analysis to clarify this issue.

METHODS

We retrieved literature from the PubMed and Embase databases, and used the NewcastleOttawa Scale to evaluate the methodological quality of the included studies. RevMan 5.3 software was used to analyse the data.

RESULTS

In total,710 articles were retrieved from the databases. Ultimately, after screening, 11 articles involving 1686 patients with epilepsy were included. Compared with that for a normal EEG, the odds ratio (OR) for an abnormal EEG was 1.10 (P=0.50), with an I2 value of 32% (P=0.15). Subgroup analysis revealed that the children-to-adolescents subgroup had an OR of 1.21 (P=0.27), and the children-to-adults subgroup had an OR of 0.64 (P=0.14) for an abnormal EEG. A separate subgroup analysis revealed that the focal epilepsy subgroup had an OR of 1.30 (P=0.37), and the generalized epilepsy and focal epilepsy subgroup had an OR of 1.07 (P=0.67) for an abnormal EEG.

CONCLUSIONS

The risk of epilepsy recurrence is not related to pretreatment EEG findings, regardless of age or epilepsy classification. The associations of pre- and posttreatment EEG alterations with epilepsy recurrence are controversial. Due to the limitations of our article, further research is needed.

Withdrawal of antiseizure medications - for whom, when, and how?

INTRODUCTION

Epilepsy is a chronic disorder of the brain characterized by an enduring predisposition to generate epileptic seizures. Most patients can achieve complete seizure control (seizure freedom) with antiseizure medications (ASMs). In some of them, the withdrawal of ASMs can be considered. Guidance is required to identify patients in whom drug discontinuation can be safely attempted and to inform when and how ASM withdrawal can be done.

AREAS COVERED

In this perspective, the authors discuss the evidence on ASM withdrawal in epilepsy patients who are seizure-free and provide some suggestions on how to do it effectively in clinical practice, minimizing the risk of seizure recurrence.

EXPERT OPINION

The decision of discontinuing ASMs in epilepsy patients should rely on an accurate estimate of seizure recurrence risk. Whenever possible, such a risk should be assessed on an individual basis. The decision should also consider the psychosocial and personal consequences of seizure relapse. No robust evidence is available on the safest tapering regimen.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.

A Computational Framework for Controlling the Self-Restorative Brain Based on the Free Energy and Degeneracy Principles

The brain is a non-linear dynamical system with a self-restoration process, which protects itself from external damage but is often a bottleneck for clinical treatment. To treat the brain to induce the desired functionality, formulation of a self-restoration process is necessary for optimal brain control. This study proposes a computational model for the brain's self-restoration process following the free-energy and degeneracy principles. Based on this model, a computational framework for brain control is established. We posited that the pre-treatment brain circuit has long been configured in response to the environmental (the other neural populations') demands on the circuit. Since the demands persist even after treatment, the treated circuit's response to the demand may gradually approximate the pre-treatment functionality. In this framework, an energy landscape of regional activities, estimated from resting-state endogenous activities by a pairwise maximum entropy model, is used to represent the pre-treatment functionality. The approximation of the pre-treatment functionality occurs via reconfiguration of interactions among neural populations within the treated circuit. To establish the current framework's construct validity, we conducted various simulations. The simulations suggested that brain control should include the self-restoration process, without which the treatment was not optimal. We also presented simulations for optimizing repetitive treatments and optimal timing of the treatment. These results suggest a plausibility of the current framework in controlling the non-linear dynamical brain with a self-restoration process.

Risk of seizure recurrence from antiepileptic drug withdrawal among seizure-free patients for more than two years

OBJECTIVE

The aim of this study was to determine the outcome of antiepileptic drug (AED) withdrawal in patients who were seizure-free for more than two years.

METHODS

Patients with epilepsy who were seizure-free for at least two years and decided to stop AED therapy gradually were followed up every two months for seizure relapse. The inclusion criteria were as follows: (1) diagnosis of epilepsy, defined as the following conditions: ① at least two unprovoked (or reflex) seizures occurring >24 h apart; ② one unprovoked (or reflex) seizure and a probability of further seizures similar to the general recurrence risk (at least 60%) after two unprovoked seizures, occurring over the next 10 years; ③ diagnosis of an epilepsy syndrome; (2) patients remained seizure-free for at least 24 consecutive months during AED therapy; and (3) patients expressed a desire to discontinue AED therapy gradually and agreed to return for regular follow-ups. The time to a seizure relapse and predictive factors were analyzed by survival methods, including sex; age at seizure onset; number of episodes; seizure-free period before AED withdrawal; duration of follow-up after AED withdrawal; AED tapering off period (taper period); results from brain magnetic resonance (MRI); electroencephalogram (EEG) after drug withdrawal; EEG before drug withdrawal; seizure type (classified as generalized, partial, or multiple types based on history); and the number of AEDs administered for long-term seizure control. A log-rank test was used for univariate analysis, and a Cox proportional hazard model was used for multivariate analysis.

RESULTS

We selected 94 patients (58 men, 36 women). The relapse ratio was 29.8%. Univariate analysis and multivariate Cox regression analysis indicated that withdrawal times and multiple AEDs, as well as the seizure-free period before withdrawal and abnormal EEG after drug withdrawal were significantly correlated with seizure recurrence and were significant independent predictive factors, with a hazard ratio of 0.839 and 3.971, 0.957, and 3.684, respectively.

SIGNIFICANCE

The relapse rate in our study was similar to commonly reported overall rates for epilepsy. Distinguishing variables, such as withdrawal times, multiple AEDs, seizure-free period before withdrawal, and abnormal EEG after drug withdrawal, need to be considered when choosing to withdraw from AEDs. Therefore, our recommendation is that after two years of seizure-free survival, patients could consider withdrawal unless they have hippocampal sclerosis (HS).

Prediction of the recurrence risk in patients with epilepsy after the withdrawal of antiepileptic drugs

Many seizure-free patients who consider withdrawing from antiepileptic drugs (AEDs) hope to discontinue treatment to avoid adverse effects. However, withdrawal has certain risks that are difficult to predict. In this study, we performed a literature review, summarized the causes of significant variability in the risk of postwithdrawal recurrent seizures, and reviewed study data on the age at onset, cause, types of seizures, epilepsy syndrome, magnetic resonance imaging (MRI) abnormalities, epilepsy surgery, and withdrawal outcomes of patients with epilepsy. Many factors are associated with recurrent seizures after AED withdrawal. For patients who are seizure-free after treatment, the role of an electroencephalogram (EEG) alone in ensuring safe withdrawal is limited. A series of prediction models for the postwithdrawal recurrence risk have incorporated various potentially important factors in a comprehensive analysis. We focused on the populations of studies investigating five risk prediction models and analyzed the predictive variables and recommended applications of each model, aiming to provide a reference for personalized withdrawal for patients with epilepsy in clinical practice.